New Relic announced the general availability of a new infrastructure monitoring experience to empower DevOps, SRE and ITOps teams to proactively identify and resolve issues in their public, private and hybrid cloud infrastructure.
The modernized experience allows engineers to instantly isolate bottlenecks by filtering and sorting based on golden signal conditions, analyze all related telemetry (including logs, events, alerts, network, etc.) in context, visualize blast radius with topology maps and perform historical analysis to understand cascading impacts. The experience is included as an essential part of the all-in-one New Relic One observability platform that allows engineers to get 3X+ more value than the competition, which requires provisioning separate SKUs with disjointed experiences and agent-based pricing models.
Detecting, investigating, and resolving infrastructure performance incidents has never been more challenging. First, there has been an exponential increase in the complexity of infrastructure to monitor as 40%+ of all enterprise workloads have moved to private, public, hybrid and edge cloud infrastructure. Second, with a majority of enterprises using Kubernetes, a large portion of infrastructure is now ephemeral, resulting in tens of thousands of components that are not feasible to monitor without modern observability. Last, infrastructure isn’t just ITOps’ responsibility, all engineers are required to be self-sufficient in debugging infrastructure-specific issues and still toggle between at least two tools to monitor the health of their systems. New Relic infrastructure monitoring capabilities address these three key issues, delivering a modern all-in-one experience that helps all engineers to troubleshoot complex distributed infrastructure.
“Our mission is to help every engineer do their best work based on data, not opinions ... We have received overwhelmingly positive feedback from our early-access customers, many of whom are replacing their incumbent solutions with New Relic One ...” said New Relic CEO Bill Staples.
New Relic’s infrastructure monitoring experience delivers five key capabilities:
- Isolate bottlenecks – View and action on queries such as “show me the hosts where CPU utilization is greater than 80%” by filtering and sorting tens of thousands of infrastructure components based on golden signal conditions.
- Access all context – Analyze related entities, change telemetry, logs, alerts, events, golden signals, network metrics, and more, all in context and in a unified experience to identify the root cause and initiate issue resolution.
- Visualize blast radius – View upstream and downstream dependencies of bottleneck components using topology maps to quantify the true extent and impact of an incident.
- Time travel analysis – Go back in time to see health status changes and cascading performance impacts on topology maps using the Timewarp module in the topology maps.
- 3X+ more value – Realize higher value on your investments as compared to the competition with agent based pricing based on New Relic One’s simple and predictable US$0.25 per GB and per-user fees.
The infrastructure monitoring experience is now generally available across all regions as part of the New Relic One platform - the all-in-one observability platform with a secure telemetry cloud, powerful full-stack analysis tools and predictable consumption pricing instead of disjointed SKU bundles. All existing customers can access this new capability without any additional cost as part of their New Relic One account.
The Latest
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...